LightGBM, short for Light Gradient Boosting Machine is ideal for large-scale data projects.
Efficient Large-Scale Learning: Optimized for performance, making it suitable for large datasets and high-dimensional data.
Fast Training: Implements novel techniques like gradient-based one-side sampling and exclusive feature bundling, speeding up training times.
Lower Memory Usage: More memory-efficient than many other gradient-boosting libraries.
Parallel and GPU Learning: Supports parallel and GPU learning, enhancing its capability to handle complex tasks.
High-Performance: Delivers high performance, both in terms of speed and accuracy, for a variety of machine learning tasks.
Flexible and Versatile: Suitable for a range of applications, from regression to classification and ranking tasks.
Active Community and Development: Benefits from ongoing development and a growing community.
What's the difference between this Python project and similar ones?
Fast Training when compared to traditional scikit-learn Gradient boosted algos.
Lower Memory Usage: More memory-efficient than many other gradient-boosting libraries.
Parallel and GPU Learning: Supports parallel and GPU learning.
--
Anyone who agrees with this pull request could submit an Approve review to it.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/2576
**Author:** [@msjithin](https://github.com/msjithin)
**Created:** 4/9/2024
**Status:** ❌ Closed
**Base:** `master` ← **Head:** `lightgbm`
---
### 📝 Commits (1)
- [`0c6c865`](https://github.com/vinta/awesome-python/commit/0c6c8657015e748cc1332e90a729a810c7c393cc) adds lightgbm
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## What is this Python project?
LightGBM, short for Light Gradient Boosting Machine is ideal for large-scale data projects.
Efficient Large-Scale Learning: Optimized for performance, making it suitable for large datasets and high-dimensional data.
Fast Training: Implements novel techniques like gradient-based one-side sampling and exclusive feature bundling, speeding up training times.
Lower Memory Usage: More memory-efficient than many other gradient-boosting libraries.
Parallel and GPU Learning: Supports parallel and GPU learning, enhancing its capability to handle complex tasks.
High-Performance: Delivers high performance, both in terms of speed and accuracy, for a variety of machine learning tasks.
Flexible and Versatile: Suitable for a range of applications, from regression to classification and ranking tasks.
Active Community and Development: Benefits from ongoing development and a growing community.
## What's the difference between this Python project and similar ones?
1. Fast Training when compared to traditional scikit-learn Gradient boosted algos.
2. Lower Memory Usage: More memory-efficient than many other gradient-boosting libraries.
3. Parallel and GPU Learning: Supports parallel and GPU learning.
--
Anyone who agrees with this pull request could submit an *Approve* review to it.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
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📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/2576
Author: @msjithin
Created: 4/9/2024
Status: ❌ Closed
Base:
master← Head:lightgbm📝 Commits (1)
0c6c865adds lightgbm📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
What is this Python project?
LightGBM, short for Light Gradient Boosting Machine is ideal for large-scale data projects.
Efficient Large-Scale Learning: Optimized for performance, making it suitable for large datasets and high-dimensional data.
Fast Training: Implements novel techniques like gradient-based one-side sampling and exclusive feature bundling, speeding up training times.
Lower Memory Usage: More memory-efficient than many other gradient-boosting libraries.
Parallel and GPU Learning: Supports parallel and GPU learning, enhancing its capability to handle complex tasks.
High-Performance: Delivers high performance, both in terms of speed and accuracy, for a variety of machine learning tasks.
Flexible and Versatile: Suitable for a range of applications, from regression to classification and ranking tasks.
Active Community and Development: Benefits from ongoing development and a growing community.
What's the difference between this Python project and similar ones?
--
Anyone who agrees with this pull request could submit an Approve review to it.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.